Fast and lightweight AI inference with Rust and WebAssembly

Fast and lightweight AI inference is becoming increasingly important as more and more devices are being equipped with AI capabilities. While Python is a popular choice for AI model training, it is not the best option for inference applications due to its complexity, dependencies, and slow execution speed. Rust and WebAssembly (Wasm) offer a unified cloud computing infrastructure that spans devices to edge cloud, on-prem servers, and the public cloud, providing a strong alternative to Python for AI inference applications.

Rust+Wasm offers ultra-lightweight, fast, and portable AI inference applications with less than 1% of the size of a typical PyTorch container, native C/Rust speed in all parts of the inference application, and easy setup, development, and deployment. The Rust+Wasm stack is safe, cloud-ready, and supports heterogeneous hardware acceleration, making it a powerful choice for AI inference applications.

With the WasmEdge GGML plugin, developers can easily deploy LLM inference applications on any device that supports the WasmEdge runtime, including Linux, Ubuntu, and Nvidia GPUs, as well as Apple M1/M2/M3’s built-in neural processing engines. The WasmEdge GGML plugin automatically takes advantage of any hardware acceleration on the device to run LLM2 models, making it a versatile and efficient choice for AI inference applications.

As the field of lightweight AI inference on the edge is still in its early stages, there are opportunities for developers to contribute to the open source projects and shape the direction of future LLM inference infrastructure. This includes adding GGML plugins for more hardware and OS platforms, supporting more llama.cpp configurations, and providing Rust+Wasm APIs for popular AI models beyond LLMs.

Developers can join the conversation and contribute to the WasmEdge discord to discuss, learn, and share insights.

Headings

Rust and WebAssembly (Wasm) offer a unified cloud computing infrastructure that spans devices to edge cloud, on-prem servers, and the public cloud, providing a strong alternative to Python for AI inference applications.

Rust+Wasm offers ultra-lightweight, fast, and portable AI inference applications with less than 1% of the size of a typical PyTorch container, native C/Rust speed in all parts of the inference application, and easy setup, development, and deployment.

The Rust+Wasm stack is safe, cloud-ready, and supports heterogeneous hardware acceleration, making it a powerful choice for AI inference applications.

With the WasmEdge GGML plugin, developers can easily deploy LLM inference applications on any device that supports the WasmEdge runtime, including Linux, Ubuntu, and Nvidia GPUs, as well as Apple M1/M2/M3’s built-in neural processing engines.

As the field of lightweight AI inference on the edge is still in its early stages, there are opportunities for developers to contribute to the open source projects and shape the direction of future LLM inference infrastructure.

Paragraphs

Fast and lightweight AI inference is becoming increasingly important as more and more devices are being equipped with AI capabilities. While Python is a popular choice for AI model training, it is not the best option for inference applications due to its complexity, dependencies, and slow execution speed. Rust and WebAssembly (Wasm) offer a unified cloud computing infrastructure that spans devices to edge cloud, on-prem servers, and the public cloud, providing a strong alternative to Python for AI inference applications. Rust+Wasm offers ultra-lightweight, fast, and portable AI inference applications with less than 1% of the size of a typical PyTorch container, native C/Rust speed in all parts of the inference application, and easy setup, development, and deployment. The Rust+Wasm stack is safe, cloud-ready, and supports heterogeneous hardware acceleration, making it a powerful choice for AI inference applications. With the WasmEdge GGML plugin, developers can easily deploy LLM inference applications on any device that supports the WasmEdge runtime, including Linux, Ubuntu, and Nvidia GPUs, as well as Apple M1/M2/M3’s built-in neural processing engines. The WasmEdge GGML plugin automatically takes advantage of any hardware acceleration on the device to run LLM2 models, making it a versatile and efficient choice for AI inference applications. As the field of lightweight AI inference on the edge is still in its